Integrative review in PhD admissions: A case study of efficiently minimizing bias while maximizing the student narrative.

Abstract

Developing scientific and medical innovations continue to be limited by lack of diverse representation among leaders and learners. One key gateway for these goals is graduate school admissions, but comprehensive consideration of all components of applications, which is needed to reduce systemic bias in admissions, is resource intensive. This case study details the conceptualization of an integrative application review process to challenge and improve classic application review frameworks which gatekeep admissions opportunities from under-represented (UR) applicants. PhD applicant cohorts to a longstanding Clinical and Translational Sciences PhD TL1 program were assessed using one of three review processes: traditional, algorithmic, or a novel integrative review process. Admissions results from each review process were pooled across matriculation years to attain a testable sample size. Effects modification models were used to assess odds of reaching each admissions phase, adjusting for UR status and review process. Results showed that classic admissions review processes were prone to bias towards admission of specific students while integrative application review did not demonstrate this trend. The Mayo Clinic Graduate School of Biomedical Sciences Clinical and Translational Sciences training program has steadily recruited and trained successful and diverse trainee cohorts over the last decade from many underrepresented backgrounds. The final adoption of an integrative application review process allows streamlined graduate school admissions of diverse student cohorts, prioritizing self-driven narratives and minimizing subjective biases where possible to allow fair assessment of learners.